Using Subsequence Mining to Identify Business Processes in Data Networks

Master's Thesis from the year 2016 in the subject Computer Science - Commercial Information Technology, grade: -, Hamburg University of Technology (TUHH; Universität zu Lübeck), language: English, abstract: To manage business processes, companies must previously define, configure, implement and enact them. Analysts try to identify companies' business processes. However, large companies might have complex business processs (BPs) and consist of many business units. Therefore, classical business process modelling hardly scales. Both, companies and analysts are interested in automated approaches for business process modelling, saving time and money. Today's business process analysts often use process mining techniques to extract company's business processes by analyzing event logs of applications. This technique has its limitations, and is strongly dependent on the kind of log files of deployed applications. By designing our mission oriented network analysis (MONA) approach using algorithms having polynomial complexity, we show that identification of business processes is tractable. Identification of related tasks which constitute business processes is based on analysis of communication patterns in network traffic. We assume that today's business processes are based on network-aided applications. Our software presents identified business processes using business process modelling notation.